Influence of high salinities on the degradation of diesel fuel by bacterial consortia
Bibliographic record
Abstract
Microbial communities from three Argentinean saline soils were extracted and tested for their ability to degrade diesel fuel in liquid culture at salinities between 0% and 25%. In each case, the degradation process was continuously monitored by measuring oxygen consumption. Two communities (CR1 and CR2) showed nearly equal degrees of degradation across a salinity range of 0%-10% (the former degrading about 63% of the diesel fuel and the latter about 70% after 53 and 80 d, respectively). Furthermore, the degree of degradation was not significantly lower in the presence of 17.5% salt (58% and 65% degraded, respectively). A third community (El Zorro) showed a maximum turnover at 5% salt (79% diesel fuel degraded) and significant degradation (66%) at a salinity of 10%. However, the degree of degradation by this community clearly dropped at 0% and 15% salt. None of the communities were able to degrade diesel fuel in the presence of 25% salt, but the living cell counts showed that components of the microbial population survived the long-term exposure. The surviving portion is obviously sufficient to allow substantial restoration of the original community, as verified by the BIOLOG method. Isolates of the CR1 community were identified as members of the genera Cellulomonas, Bacillus, Dietzia, and Halomonas. In light of our investigations, the bioremediation of contaminated saline soils should be quite possible if the salinity of the soil water is lower than 15% or if it is reduced below this limit by the addition of water.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".